Image identification and selection for motion correction and peak enhancement

ABSTRACT

Methods and systems for performing motion correction on imaging data. The methods include accessing a set of imaging data. The set of imaging data includes first imaging data for a first time point, second imaging data for a second time point, and third imaging data for a third time point. A location of a region of interest (ROI) is identified in the different imaging data. Differences between the locations of ROI across the imaging data are determined and aggregated to generate an aggregate motion score for the respective imaging data in the set of imaging data. One of the imaging data is then selected as reference imaging data for motion correction based on the aggregate motion score. Motion correction of the set of imaging data is performed based on the selected reference imaging data. Similar comparisons on images may be performed for peak enhancement of MRI imaging data.

This application is being filed on Mar. 13, 2020, as a PCT InternationalPatent Application and claims the benefit of U.S. Patent ApplicationSer. No. 62/818,449, filed on Mar. 14, 2019, the disclosure of which ishereby incorporated herein by reference in its entirety

BACKGROUND

Medical imaging has become a widely used tool for identifying anddiagnosing abnormalities, such as cancers or other conditions, withinthe human body. For example, magnetic resonance imaging (MRI) is oneimaging modality that may be used for medical applications. In MRI,three dimensional (i.e., volumetric) imaging information of a patient'sbody region is acquired for diagnostic purposes. Other imagingmodalities, such as computed tomography (CT), positron emissiontomography (PET), mammography, and tomosynthesis, are also available formedical imaging procedures. In some imaging modalities, the images maybe acquired at multiple points in time, often referred to as dynamicimaging. For instance, MRI information may be acquired at multiplepoints in time in order to study the time progression of dynamicprocesses, such as the movement of blood. Tomosynthesis involvesacquiring images of a portion of the patient, such as the patient'sbreast, at multiple angles at different time points. In some instances,contrast-enhanced or dual-energy mammography may also involve acquiringimages at multiple different time points. During such dynamic imagingprocedures where images are acquired over time, the patient may moveduring the time required to complete the imaging procedure. If thepatient moves during the imaging procedure, the acquired imaging datamay need to be corrected to account for such movement of the patient.

It is with respect to these and other general considerations that theaspects disclosed herein have been made. Also, although relativelyspecific problems may be discussed, it should be understood that theexamples should not be limited to solving the specific problemsidentified in the background or elsewhere in this disclosure.

SUMMARY

Examples of the present disclosure describe systems and methods forimproving imaging procedures and resultant image quality from suchimaging procedures. In an aspect, the technology relates to a methodcomprising accessing a set of imaging data including first imaging datafor a first time point, second imaging data for a second time point, andthird imaging data for a third time point. The method further includesidentifying a first location of a region of interest (ROI) in the firstimaging data; identifying a second location of the ROI in the secondimaging data; and identifying a third location of the ROI in the thirdimaging data. The method also includes determining a plurality ofdifferences between the identified locations, wherein the plurality ofdifferences include a difference between: the first location and thesecond location; the first location and the third location; and thesecond location and the third location. The method additionallyincludes, based on the determined plurality of differences, selectingone of the first imaging data, the second imaging data, or the thirdimaging data as reference imaging data for motion correction, andperforming motion correction for the set of imaging data with theselected reference image.

In an example, the set of imaging data is magnetic resonance imaging(MRI) data. In another example, the set of imaging data includestwo-dimensional medical images and the locations of the ROI include afirst coordinate corresponding to a first dimension and a secondcoordinate corresponding to a second dimension. In still anotherexample, determining the difference between the first location and thesecond location comprises: determining the difference between the firstcoordinate of the first location and the first coordinate of the secondlocation; and determining the difference between the second coordinateof the first location and the second coordinate of the second location.In yet another example, the set of imaging data includesthree-dimensional medical images and the locations of the ROI include afirst coordinate corresponding to a first dimension, and a secondcoordinate corresponding to a second dimension, and a second coordinatecorresponding to a third dimension. In still yet another example,determining the difference between the first location and the secondlocation comprises: determining the difference between the firstcoordinate of the first location and the first coordinate of the secondlocation; determining the difference between the second coordinate ofthe first location and the second coordinate of the second location; anddetermining the difference between the third coordinate of the firstlocation and the third coordinate of the second location. In anotherexample, determining the difference between the first location and thesecond location comprises determining a distance between the firstlocation and the second location.

In another aspect, the technology relates to a system comprising: adisplay; at least one processor; and memory storing instructions that,when executed by the at least one processor, cause the system to performa set of operations. The set of operations includes accessing a set ofimaging data including first imaging data for a first time point, secondimaging data for a second time point, and third imaging data for a thirdtime point; identifying a first location of a region of interest (ROI)in the first imaging data; identifying a second location of the ROI inthe second imaging data; and identifying a third location of the ROI inthe third imaging data. The set of operations also includes determininga plurality of differences between the identified locations, wherein theplurality of differences include a difference between: the firstlocation and the second location; the first location and the thirdlocation; and the second location and the third location. The set ofoperations further includes based on the determined plurality ofdifferences, selecting one of the first imaging data, the second imagingdata, or the third imaging data as reference imaging data for motioncorrection; performing motion correction for the set of imaging datawith the selected reference imaging data; and displaying at least aportion of the motion corrected set of imaging data on the display.

In an example, the system further includes a medical imaging apparatusand the set of operations further comprise acquiring the set of imagingdata from the medical imaging apparatus. In another example, the medicalimaging apparatus is a magnetic resonance imaging (MRI) machine and theimaging data includes MRI volumes. In still another example, the set ofimaging data includes two-dimensional medical images and the locationsof the ROI include a first coordinate corresponding to a first dimensionand a second coordinate corresponding to a second dimension, anddetermining the difference between the first location and the secondlocation comprises: determining the difference between the firstcoordinate of the first location and the first coordinate of the secondlocation; and determining the difference between the second coordinateof the first location and the second coordinate of the second location.In yet another example, the medical images are three-dimensional medicalimages and the locations of the ROI include a first coordinatecorresponding to a first dimension, and a second coordinatecorresponding to a second dimension, and a second coordinatecorresponding to a third dimension, and determining the differencebetween the first location and the second location comprises:determining the difference between the first coordinate of the firstlocation and the first coordinate of the second location; determiningthe difference between the second coordinate of the first location andthe second coordinate of the second location; and determining thedifference between the third coordinate of the first location and thethird coordinate of the second location. In still yet another example,determining the difference between the first location and the secondlocation comprises determining a distance between the first location andthe second location.

In another aspect, the technology relates to a method that includesaccessing a set of medical images acquired at a plurality of timepoints. The method also includes identifying a region of interest (ROI)in at least half of the medical images in the set of medical images;identifying a location of the ROI in the at least half of the medicalimages in the set of medical images; comparing the identified locationsof the ROI in at least one pair of the medical images of the set ofmedical images; based on the comparisons of the identified locations,selecting one of the medical images of the set of medical images to be areference image for motion correction; and performing motion correctionof the set of the medical images with the selected reference image.

In an example, the comparing operation comprises comparing theidentified locations of the ROI in a plurality of pairs of medicalimages in the set of medical images. In another example, the pluralityof pairs of medical images include all possible pairs of medical imagesin the set of medical images. In still another example, the medicalimages are two-dimensional medical images and the locations of the ROIinclude a first coordinate corresponding to a first dimension and asecond coordinate corresponding to a second dimension. In yet anotherexample, the medical images are three-dimensional medical images and thelocations of the ROI include a first coordinate corresponding to a firstdimension, and a second coordinate corresponding to a second dimension,and a second coordinate corresponding to a third dimension.

In another aspect, the technology relates to accessing a set of imagingdata including first imaging data for a first time point, second imagingdata for a second time point, and third imaging data for a third timepoint. The method also includes identifying a first location of a regionof interest (ROI) in the first imaging data; identifying a secondlocation of the ROI in the second imaging data; and identifying a thirdlocation of the ROI in the third imaging data. The method furtherincludes determining a first difference between the first location andthe second location; determining a second difference between the firstlocation and the third location; and determining a third differencebetween the second location and the third location. The method alsoincludes aggregating the first difference and the second difference togenerate a first aggregate motion score for the first imaging data;aggregating the first difference and the third difference to generate asecond aggregate motion score for the second imaging data; andaggregating the second difference and the third difference to generate athird aggregate motion score for the third imaging data. The methodadditionally includes, based on the first aggregate motion score, thesecond aggregate motion score, and the third aggregate motion score,selecting one of the first imaging data, the second imaging data, or thethird imaging data as reference imaging data for motion correction; andperforming motion correction of the set of imaging data based on theselected reference imaging data.

In an example, the first difference is a distance between the firstlocation and the second location; the second difference is a distancebetween the first location and the third location; and the thirddifference is a distance between the second location and the thirdlocation. In another example, the first difference is an area betweenthe first location and the second location; the second difference is anarea between the first location and the third location; and the thirddifference is an area between the second location and the thirdlocation.

In another aspect, the technology relates to a method that includesaccessing a set of imaging data including first imaging data for a firsttime point, second imaging data for a second time point, and thirdimaging data for a third time point; identifying an outline of a regionof interest (ROI) in the first imaging data; identifying an outline ofthe ROI in the second imaging data; identifying an outline of the ROI inthe third imaging data; determining a first area between the firstoutline and the second outline; determining a second area between thefirst outline and the third outline; determining a third area betweenthe second outline and the third outline; aggregating the first area andthe second area to generate a first aggregate motion score for the firstimaging data; aggregating the first area and the third area to generatea second aggregate motion score for the second imaging data; aggregatingthe second area and the third area to generate a third aggregate motionscore for the third imaging data; based on the first aggregate motionscore, the second aggregate motion score, and the third aggregate motionscore, selecting one of the first imaging data, the second imaging data,or the third imaging data as reference imaging data for motioncorrection; performing motion correction of the set of imaging databased on the selected reference imaging data; and displaying at least aportion of motion corrected set of imaging data.

In an example, the first area is a non-overlapping area between thefirst outline and the second outline; the second area is anon-overlapping area between the first outline and the third outline;and the third area is a non-overlapping area between the second outlineand the third outline. In another example, selecting one of the firstimaging data, the second imaging data, or the third imaging data as thereference imaging data for motion correction comprises selecting theimaging data with the lowest aggregate motion score. In still anotherexample, the first area is an overlapping area between the first outlineand the second outline; the second area is an overlapping area betweenthe first outline and the third outline; and the third area is anoverlapping area between the second outline and the third outline. Inyet another example, selecting one of the first imaging data, the secondimaging data, or the third imaging data as the reference imaging datafor motion correction comprises selecting the imaging data with thehighest aggregate motion score. In still yet another example, the ROI isa skin line of a breast.

In another example, the technology relates to a method the includesaccessing a set of MRI imaging data, the set of MRI imaging dataincluding at least a first volume acquired at a first time and a secondvolume acquired at a second time; identifying at least one localcontrast enhancement region in the first volume; identifying the atleast one local contrast enhancement region in the second volume;evaluating contrast dynamics for the local contrast enhancement regionin the first volume; evaluating contrast dynamics for the local contrastenhancement region in the second volume; based on the evaluated contrastdynamics for the local contrast enhancement region in the first volumeand the second volume, selecting either the first volume or the secondvolume as the peak enhancement volume; and performing the colorizationfor the set of MRI imaging data based on the peak enhancement volume.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Additionalaspects, features, and/or advantages of examples will be set forth inpart in the description which follows and, in part, will be apparentfrom the description, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive examples are described with reference tothe following figures.

FIG. 1 depicts an example set of images acquired during a dynamicimaging procedure.

FIG. 2A depicts another example set of images acquired during a dynamicimaging procedure.

FIG. 2B depicts a difference in regions of interest (ROIs) in twoimages.

FIG. 2C depicts an example of the non-overlapping area between twooutlines of a skin line of a breast.

FIG. 3 depicts an example method for performing motion correction ofmedical images.

FIG. 4 depicts an example method for performing motion correction ofmedical images.

FIGS. 5A and 5B depict an example method for performing motioncorrection of medical images.

FIG. 6 depicts an example method for performing colorization of MRIimages.

FIG. 7 depicts an example of a system for the medical imagingtechnologies discussed herein.

DETAILED DESCRIPTION

Dynamic imaging is a useful tool that acquires medical images atmultiple points in time. These medical images may be acquired during asingle scan or imaging procedure of the patient. For example, MRI imagesmay be acquired in a series to study the time progression of dynamicprocesses, such as the movement of blood. Dynamic contrast enhanced(DCE) MRI is one such example of dynamic MRI imaging. In DCE MRI,multiple MRI volumes are acquired following the intravenous injection ofa contrast agent. Each acquired volume shows how the contrast agent hasprogressed through the patient at the respective time point at which thevolume was acquired. Tomosynthesis involves acquiring images of aportion of the patient, such as the patient's breast, at multiple anglesat different time points. During such dynamic imaging procedures, thepatient may move during the time required to complete the imagingprocedure. Patient movement is also more likely where the duration ofthe scan is longer, and longer scans may also acquire more images thanshorter scans. Each individual patient may also move in a differentmanner during his or her individual scan. Patient movement during anydynamic imaging procedure, however, often degrades the ultimate imagequality of the images produced from the imaging procedure. For example,in DCE MRI, the dynamic processes that are intended to be seen may bemore difficult to view as the multiple images are misaligned due to themovement of the patient.

Accordingly, it is desirable to have processes that allow for the imagesacquired during dynamic imaging procedures to be corrected for motion.In some motion correction algorithms, motion is corrected by shiftingsubsequent images to match the first image. For instance, in an exampleMRI procedure, four images may be acquired at different time points. Tocorrect for motion during the procedure, the last three images may beshifted to align with the first image. Such a default process, however,may result to extensive correction of movement of the patient and mayfail to account for different types of movement of the patient.Extensive motion correction to any image may reduce the resultant imagequality and affect the potential clinical accuracy available from theimage, particularly when diagnosing small lesions.

The present technology improves on such motion correction processes byaccounting for the particular motion of the patient that has been imagedand reducing the total amount of motion correction that needs to beperformed for a set of medical images. For instance, the presenttechnology analyzes the locations of markers or regions of interest(ROIs) identified in each of the images acquired during a dynamicimaging procedure. As an example, the location of an ROI in a firstimage may be compared to the location of the ROI in each of the otheracquired images. Similarly, the location of the ROI in a second imagemay be compared to the location of the ROI in each of the other acquiredimages. The comparison of ROI locations may involve determining adistance between the relative ROI locations in each respective image.Based on the comparisons of the ROI locations, one of the images is thenselected as a reference, or base, image to perform the motioncorrection. For instance, the image that would reduce the total amountof image correction for entire set of images may be selected as thereference image for motion correction. The motion correction procedureis then performed using the selected image as the reference image, whichresults in less overall image correction required for dynamic imagingprocesses.

The present technology is also capable of analyzing multiple images orMRI volumes to identify most suitable volume to use as the peakenhancement volume for colorization of the portion of the patient beingimaged, such as a breast. In general, colorization of breast MRI lesionsis based on determining contrast dynamics within the imaged breasttissue prior to and the peak concentration of contrast has beenachieved. In many current MRI colorization systems, a fixed timeoutperiod, such as 120 seconds, is used to select the MRI volume. Thus, thesame volume was always used as the peak enhancement volume. Forinstance, the volume acquired closest to the fixed timeout period wasselected by default as the peak enhancement volume without any analysisof the volumes themselves. The timeout period, however, differs betweendifferent types of MRI imaging apparatuses, and the different types ofMRI imaging apparatuses required different settings for the timeoutperiod. That variability between imaging apparatuses also raised thepotential to mislabel a volume as the peak enhancement volume,especially when taking into account the variability of tissue dynamics.

The present technology improves the peak volume selection process byactually analyzing the multiple MRI volumes acquired during a DCE MRIprocedure. For instance, the present technology may evaluate thecontrast dynamics of each acquired volume to more accurately select thevolume that shows the peak enhancement properties. As such, thecolorization and concentration curves for the set of MRI volumes may bebased on a more accurate selection of a peak enhancement volume ratherthan a default time point.

FIG. 1 depicts an example set of images 100 acquired during a dynamicimaging procedure. The set of images 100 includes a first image 102 thatwas acquired at a first time point, a second image 104 that was acquiredat a second time point, a third image 106 that was acquired at a thirdtime point, and a fourth image 108 that was acquired at a fourth timepoint. Each image in the set includes a depiction of a breast 110. Aregion of interest (ROI) 112 has also been identified in each of theimages. In the example depicted, the ROI is the centroid of the breast110. The ROI, however, may be different in other examples and may be anyidentifiable feature of the breast or portion of the patient that isbeing imaged. In examples where a breast is imaged, the ROI may be theskin line, the nipple, chest wall, or other fiduciary markers. As can beseen from the set of images 100, the breast 110 and the region ofinterest has moved in each of the images. Accordingly, the patient hasmoved between the time the first image 102 was acquired and the time thefourth image 108 was acquired, and some form of motion correction isdesired.

The present technology identifies the most appropriate image of the setof images 100 to be used as the reference image for motion correctionprocesses. For example, the reference image is the image to which theremaining references are altered to match, or at least be altered tomove closer to the position of the breast 110 in the reference image. Toselect the reference image, an analysis of the locations of the ROI 112across the images is performed.

In the example depicted, the ROI 112 in the first image is located at anx-coordinate of 0 and a y-coordinate of 0. Typical coordinate notationof (x,y) may be used herein. As such, the location of the ROI 112 in thefirst image 102 may be referred to as (x1,y1) and has a value of (0,0).The location of the RO1 112 in the second image 104 is represented as(x2, y2) and has a value of (3,0). The location of the ROI 112 in thethird image 106 is represented as (x3, y3) and has a value of (2,0), andthe location of the ROI 112 in the fourth image 108 is (x4, y4) and hasa value of (4,0). Accordingly, in the example depicted, the breast 112shifted only in the x-direction.

To determine which image of the set of images 100 is best suited to beselected as the reference image, a comparison of the location of the ROI112 in each image is compared to location of the ROI 112 in theremaining image. The comparisons may be used or aggregated to determinean aggregate motion score for each of the images. The image of the setof images 100 that has the lowest aggregate motion score may be selectedas the reference image.

To determine the aggregate motion score for the first image 102, thedifference between the location of the ROI 112 in the first image 102and the locations of the ROI 112 in the remaining images is determined.For instance, the differences and the aggregate motion score for thefirst image 102 are as follows:

TABLE 1 First Image Analysis Representative Resultant ComparisonEquation Value First Image vs. Second Image x₁ − x₂ 3 First Image vs.Third Image x₁ − x₃ 2 First Image vs. Fourth Image x₁ − x₄ 4 AggregateMotion Score for First Image 9

As can be seen from the above table, the aggregate motion score for thefirst image is the sum or aggregation of the differences in location ofthe ROI 112 between the first image 102 and the remaining images. Ofnote, in the example depicted, because there was no movement in they-direction of the breast 110, calculations regarding the y-coordinatehave been omitted as each of the differences would have been zero. Inaddition, the differences in the present example are generally taken asthe absolute value of the differences such that negative values may beavoided.

The differences and the aggregate motion score for the second image 104are as follows:

TABLE 2 Second Image Analysis Representative Resultant ComparisonEquation Value Second Image vs. First Image x₂ − x₁ 3 Second Image vs.Third Image x₂ − x₃ 1 Second Image vs. Fourth Image x₂ − x₄ 1 AggregateMotion Score for Second Image 5

The differences and the aggregate motion score for the third image 106are as follows:

TABLE 3 Third Image Analysis Representative Resultant ComparisonEquation Value Third Image vs. First Image x₃ − x₁ 2 Third Image vs.Second Image x₃ − x₂ 1 Third Image vs. Fourth Image x₃ − x₄ 2 AggregateMotion Score for Third Image 5

The differences and the aggregate motion score for the fourth image 108are as follows:

TABLE 4 Fourth Image Analysis Representative Resultant ComparisonEquation Value Fourth Image vs. First Image x₄ − x₁ 4 Fourth Image vs.Second Image x₄ − x₂ 1 Fourth Image vs. Third Image x₄ − x₃ 2 AggregateMotion Score for Fourth Image 7

The following table provides a summary of each of the aggregate motionscores:

TABLE 5 Aggregate Motion Score Summary of Tables 1-4 Aggregate MotionImage Number Score First Image 9 Second Image 5 Third Image 5 FourthImage 7

Accordingly, the second image 104 and third image 106 have the lowestaggregate motion scores (5), followed by the fourth image 108, with anaggregate motion score of 7, and the first image 102 with an aggregatemotion score of 9. Thus, in this example, one of the second image 104 orthe third image 108 is selected as the representative image for motioncorrection. In some examples, such as the present example, there is atie between two images for the lowest aggregate motion scores. To breakthe tie, a maximum difference between any pair may be utilized. Forinstance, in the present example, the calculated differences for thesecond image 104 were 3, 1, and 1. The calculated differences for thethird image 106 were 2, 1, and 2. Therefore, the maximum difference forthe second image was 3 and the maximum difference for the third imagewas 2. The image having the lowest maximum difference is selected as thereference image for motion correction. Consequently, in the presentexample, the third image 106 is selected as the reference. It is worthnoting that, based on the aggregate motion scores, the first image 102would have been the worst selection for use as a reference image becauseusing the first image would have required the most amount of motioncorrection across the set of images 100. As such, the analysis of themultiple pairs of images within the set of images 100 allows for anidentification of a reference image that requires less than motioncorrection than if an arbitrary reference image was selected, whichresults in improved image quality of the set of images 100 after motioncorrection. The analysis or more pairs of images may also result inimproved accuracy in the selection of the most appropriate image for useas the reference image.

FIG. 2 depicts another depicts an example set of images 200 acquiredduring a dynamic imaging procedure. The set of images 200 includes afirst image 202 that was acquired at a first time point, a second image204 that was acquired at a second time point, a third image 206 that wasacquired at a third time point, and a fourth image 208 that was acquiredat a fourth time point. Each image in the set includes a depiction of abreast 210. An ROI 212 has also been identified in each of the images.In the example depicted, the ROI is the centroid of the breast 210. Ascan be seen from the set of images 200, the breast 210 and the region ofinterest has moved in each of the images. The example depicted in FIG. 2differs from the example depicted in FIG. 2 in that the breast 210movement in FIG. 2 is in two dimensions.

In the example depicted in FIG. 2, the ROI 212 in the first image islocated at (0,0). The location of the ROI 212 in the second image 204 is(3,1), the location of the ROI 212 in the third image 206 is (2,−2), andthe location of the ROI 212 in the fourth image 208 is (−2, 1).Determining the difference between each of the pairs of images in theset of images 200 may be performed by calculated the distance from theROI 212 locations in each of the images. One example equation forcalculating the distance between two ROI 212 locations is as follows:

$D = \sqrt{\left( {x_{1} - x_{2}} \right)^{2} + \left( {y_{1} - y_{2}} \right)^{2}}$

In the above equation, D is the distance, x₁ is x-coordinate of a firstROI 212 location, y₁ is the y-coordinate of the first ROI 212 location,x₂ is the x-coordinate of a second ROI 212 location, and y₂ is they-coordinate of the second ROI 212 location. Of note, the above distanceequation could have been used to calculate distance in the example inFIG. 1, and the same results would have been achieved.

The differences and the aggregated motion score for the first image 202are thus as follows:

TABLE 6 First Image Analysis Resultant Comparison RepresentativeEquation Value First Image vs. Second Image {square root over((x₁ − x₂)² + (y₁ − y₂)²)} 3.16 First Image vs. Third Image {square rootover ((x₁ − x₃)² + (y₁ − y₃)²)} 2.83 First Image vs. Fourth Image{square root over ((x₁  −  x₄)² + (y₁ − y₄)²)} 2.23 Aggregate MotionScore for First Image 8.22

The differences and the aggregate motion score for the second image 204are as follows:

TABLE 7 Second Image Analysis Resultant Comparison RepresentativeEquation Value Second Image vs. First Image {square root over((x₂ − x₁)² + (y₂ − y₁)²)} 3.16 Second Image vs. Third Image {squareroot over ((x₂ − x₃)² + (y₂ − y₃)²)} 3.16 Second Image vs. Fourth Image{square root over ((x₂ − x₄)² + (y₂ − y₄)²)} 5 Aggregate Motion Scorefor Second Image 11.32

The differences and the aggregate motion score for the third image 206are as follows:

TABLE 8 Third Image Analysis Resultant Comparison RepresentativeEquation Value Third Image vs. First Image {square root over((x₃ − x₁)² + (y₃ − y₁)²)} 2.82 Third Image vs. Second Image {squareroot over ((x₃ − x₂)² + (y₃ − y₂)²)} 3.16 Third Image vs. Fourth Image{square root over ((x₃ − x₄)² + (y₃ − y₄)²)} 5 Aggregate Motion Scorefor Third Image 10.98

The differences and the aggregate motion score for the fourth image 208are as follows:

TABLE 9 Fourth Image Analysis Resultant Comparison RepresentativeEquation Value Fourth Image vs. First Image {square root over((x₄ − x₁)² + (y₄ − y₁)²)} 2.23 Fourth Image vs. Second Image {squareroot over ((x₄ − x₂)² + (y₄ − y₂)²)} 5 Fourth Image vs. Third Image{square root over ((x₄ − x₃)² + (y₄ − y₃)²)} 5 Aggregate Motion Scorefor Fourth Image 10.23

The following table provides a summary of each of the aggregate motionscores:

TABLE 10 Aggregate Motion Score Summary of Tables 6-9 Aggregate MotionImage Number Score First Image 8.22 Second Image 11.32 Third Image 10.98Fourth Image 10.23As can be seen from the above table, the first image 102 has the lowestaggregate motion score. Thus, the first image 102 is selected as thereference image for motion correction.

FIG. 2B depicts a difference in ROIs in two images. In particular, anoutline of the skin line 216A of the breast 210 from image 202 is shownoverlaid on top of an outline of the skin line 216B of the breast 210from image 204 depicted in FIG. 2A. In some examples, the presenttechnology may use the outline of an ROI, such as the skin line of abreast, the chest wall, or other identifiable markers having outlines,to determine the differences between the locations of the ROI in each ofthe images. Determining the difference between two outlines may includedetermining the area that are not common to both outlines. FIG. 2Cdepicts an example of the area between two outlines of a skin line of abreast. The area that is not common, or overlapping, is designated bythe hashed lines. That non-overlapping area may be determined for eachpair of images in a set of images. The determination of thenon-overlapping area may be made by overlaying the two outlines andcalculating the areas in the outlines that do not overlap. Such adetermination may be made through image analysis processes. The area mayalso be determined by representing each outline as a curve or functionand then taking the integral between the difference in the two curve orfunctions. For example, for the pair of outlines 216A, 216B representedin FIGS. 2B-2C, the first outline 216A may be represented as a firstfunction f(x) and the second outline 216B may be represented as a secondfunction (x). The area between the two functions may be determined bytaking the integral of the difference of the two functions, such asf(f(x)−g(x))dx. Depending on the implementation, an integral may need tobe calculated between each intersection point of the two functions todetermine the total area.

For each image, an aggregate motion score may also be calculated that isbased on an aggregate of the differences in areas for each pair ofimages. Such a determination is similar to aggregate motion scoresdiscussed above that related to determining the distance between twopoints. In examples where an outline of an ROI is used, the aggregatemotion score may be based on the areas between the two outlines. Theimage with the lowest aggregate motion score is then selected as thereference image for performing motion correction.

As should be appreciated, while the above examples set of images includeonly four images, the process for selecting the most appropriatereference image for motion correction may be applied to sets of imageshaving a greater or fewer number of references. As an example, for eachimage in a set of images (no matter the number of images), thedifference between the locations of the ROI for each possible pair ofimages in the set may be determined. The differences may be then beaggregated to determine an aggregate motion score for each image in theset of images. The image with the lowest aggregate motion score is thenselected as the reference image for motion correction.

In addition, in the above examples in FIG. 1 and FIG. 2A-C, the sets ofimages have been two-dimensional images. For instance, the images may berepresentative of slices from an MRI volume. The first image may be aslice from a first MRI volume taken at the first time point, the secondimage may be a slice from a second MRI volume taken at the second timepoint, the third image may be a slice from a third MRI volume taken atthe third time point, and the fourth image may be a slice from thefourth MRI volume taken at the fourth time point. When the particularimage is selected as the reference image, the entire correspondingvolume may be selected as the reference volume for performing motioncorrection. For example, if the third image is selected as the referenceimage, the MRI volume acquired at the third time point may be selectedas the reference volume for performing motion correction.

In other examples, analysis and comparison of ROI locations may be doneacross three-dimensional images or image data. For instance, a threedimensional location of an ROI may be identified in a first volume andlocations of the ROI may be identified in other volumes. The location ofthe ROI may be represented in three-dimensional Cartesian coordinates(x, y, z). The location of the ROI in a first volume may then berepresented as (x₁, y₁ z₁) and the location of the ROI in a secondvolume may be represented as (x₂, y₂, z₂). The distance between twolocations of an ROI may be determined with the following equation:

$D = \sqrt{\left( {x_{1} - x_{2}} \right)^{2} + \left( {y_{1} - y_{2}} \right)^{2} + \left( {z_{1} - z_{2}} \right)^{2}}$

As in the above examples, a difference between the locations of the ROImay be determined for all possible pairs of volumes in a set of volumes.The differences for each volume may be aggregated to determine anaggregate motion score for each of the volumes, and the volume with thelowest aggregate motion score is selected as the reference volume formotion correction.

Further, while in the examples above only a single ROI was identified,in other examples multiple ROIs in each image may be identified. Forexample, a first ROI may be identified and a second ROI may beidentified in each image. The process for determining the differencebetween the locations of the first ROI and the calculation of thedifferences of the locations may be determined as discussed above. Thesame process may then be performed for the second ROI. The aggregatemotion score for each image may then be a combination of the determineddifferences for the first ROI and the second ROI.

FIG. 3 depicts an example method 300 for performing motion correction ofmedical images. At operation 302, a set of imaging data is accessed.Accessing the set of imaging data may include acquiring the images froma medical imaging apparatus and/or receiving the medical images fromanother storage source. The set of imaging data may include firstimaging data for a first time point, second imaging data for a secondtime point, and third imaging data for a third time point. Additionalimaging date may also be included in the set of imaging data. The set ofimaging data may include, for example, MRI imaging data such as MRIvolumes and/or slices of MRI volumes. At operation 304, a first locationof an ROI in the first imaging data is identified. The identification ofan ROI in the imaging data may be performed via computer-aided detectionthat analyzes the imaging data to identify the particular ROI in theimaging data and determine its location and coordinates. At operation306, a second location for the ROI in the second imaging data isidentified, and at operation 308, a third location of the ROI isidentified in the third imaging data. The second location of the ROI isthe location of the ROI in the second imaging data. Similarly, the thirdlocation of the ROI is the location of the ROI in the third imagingdata. In some examples, the set of imaging data includes two-dimensionalmedical images and the locations of the ROI include a first coordinatecorresponding to a first dimension (e.g., the x dimension) and a secondcoordinate corresponding to a second dimension (e.g., they dimension).For instance, the locations of the ROI may be represented as coordinatessuch as (x, y). In other examples, the set of imaging data may includethree-dimensional medical images, and the locations of the ROI include afirst coordinate corresponding to a first dimension (e.g., the xdimension), and a second coordinate corresponding to a second dimension(e.g., the y-dimension), and a second coordinate corresponding to athird dimension (e.g., the z dimension). For instance, the locations ofthe ROI may be represented as coordinates such as (x, y, z). Thedifferences determined may be distances from one location to another,and such distances may be calculated or determined using the abovedistance equations.

At operation 310, a plurality of differences between the identifiedlocations of the ROI are determined. For instance, the differencebetween (1) the first location and the second location, (2) the firstlocation and the third location, and (3) the second location and thethird location may all be determined. As an example, where the set ofimaging data includes two-dimensional images, determining the differencebetween the first location and the second location may includedetermining the difference between the first coordinate of the firstlocation and the first coordinate of the second location, anddetermining the difference between the second coordinate of the firstlocation and the second coordinate of the second location. In an examplewhere the set of imaging data includes three-dimensional images,determining the difference between the first location and the secondlocation may include determining the difference between the firstcoordinate of the first location and the first coordinate of the secondlocation, determining the difference between the second coordinate ofthe first location and the second coordinate of the second location, anddetermining the difference between the third coordinate of the firstlocation and the third coordinate of the second location.

Based on the determined plurality of differences in operation 310, thereference imaging data is selected at operation 312. For instance,either the first imaging data, the second imaging data, or the thirdimaging data is selected to be the reference imaging data for motioncorrection. The determination may be based on an aggregate motion scorefor each of the imaging data, and that aggregate motion score is basedon the determined plurality of differences. At operation 314, motioncorrection is performed based on the imaging data selected as thereference imaging data in operation 312. For example, the imaging dataother than the selected imaging data may be altered to more closelymatch the selected imaging data. Performing the motion correction mayresult in a motion-corrected set of imaging data. At operation 316, atleast a portion of the motion-corrected set of imaging data may bedisplayed. The motion-corrected set of imaging data may also be storedlocally or remotely. The motion-corrected set of imaging data may alsobe used in completing or performing the clinical task or review forwhich the imaging procedure was performed. The motion-corrected set ofimaging data may also be used for computing or generating final images,such as in MRI or tomosynthesis procedures. For instance, areconstruction of a tomosynthesis volume may be generated from themotion-corrected set of imaging data and/or tomosynthesis slices may begenerated from the motion-corrected set of imaging data.

FIG. 4 depicts another method 400 for performing motion correction. Atoperation 402, a set of medical images is accessed. The set of medicalimages includes medical images that were acquired at a plurality ofdifferent time points. At operation 404, an ROI is identified in each ofthe medical images in the set of medical images. In some examples, theROI may be identified in at least a majority, or about half, of themedical images in the set of medical images. At operation 406, alocation of the ROI in each of the medical images is identified. In someexamples, the location of the ROI is identified for each image where theROI was identified in operation 404. The identification of the ROI andthe identification of the location of the ROI may be performed usingcomputer-aided detection.

At operation 408, the locations of the ROI identified at operation 406are compared. As an example, the locations of the ROI in at least onepair of medical images are be compared. In other examples, the locationof the ROI may be compared for a plurality of pairs of medical images inthe set of medical images. For instance, the plurality of pairs mayinclude all possible pairs of medical images in the set of medicalimages. At operation 410, the reference image for motion correction isselected based on the comparisons of the locations of the ROI inoperation 408. At operation 412, motion correction is performed on theset of medical images based on the set of medical images. Performing themotion correction may result in a motion-corrected set of medicalimages. At operation 414, at least a portion of the motion-corrected setof medical images may be displayed. The motion-corrected set of medicalimages may also be stored locally or remotely. The motion-corrected setof imaging data may also be used in completing or performing theclinical task or review for which the imaging procedure was performed.The motion-corrected set of imaging data may also be used for computingor generating final images, such as in MRI or tomosynthesis procedures.For instance, a reconstruction of a tomosynthesis volume may begenerated from the motion-corrected set of imaging data and/ortomosynthesis slices may be generated from the motion-corrected set ofimaging data.

FIG. 5A depicts another method 500 for performing motion correction. Atoperation 502, a set of imaging data is accessed. The imaging dataincludes at least first imaging data for a first time point, secondimaging data for a second time point, and third imaging data for a thirdtime point. At operation 504, a first location of an ROI is identifiedin the first imaging data. At operation 506, a second location of an ROIis identified in the second imaging data. At operation 508, a thirdlocation of the ROI is identified in the third imaging data.

At operation 510, a first difference is determined between the firstlocation and the second location. At operation 512, a second differenceis determined between the first location and the third location. Atoperation 514, a third difference is determined between the secondlocation and the third location. At operation 516, the first differenceand the second difference are aggregated to generate a first aggregatemotion score for the first imaging data. At operation 518, the firstdifference and the third difference are aggregated to generate a secondaggregate motion score for the second imaging data. At operation 520,the second difference and the third difference are aggregated togenerate a third aggregate motion score for the third imaging data.

At operation 522, reference imaging data is selected based on the firstcomposite motion score, the second composite motion score, and the thirdcomposite motion score. For instance, the imaging data that has thelowest aggregate motion score may be selected as the reference imagingdata for motion correction. Thus, one of the first imaging data, thesecond imaging data, or the third imaging data as reference imaging datafor motion correction in operation 522. At operation 524, motioncorrection is performed on the set of imaging data based on the selectedreference imaging data. The motion corrected imaging data may also thenbe displayed.

FIG. 5B depicts another method 550 for performing motion correction. Atoperation 552, a set of imaging data is accessed. The imaging dataincludes at least first imaging data for a first time point, secondimaging data for a second time point, and third imaging data for a thirdtime point. At operation 554, a first outline of an ROI is identified inthe first imaging data. At operation 556, a second outline of an ROI isidentified in the second imaging data. At operation 558, a third outlineof the ROI is identified in the third imaging data.

At operation 560, a first area is determined between the first outlineand the second outline. The area determined may be the non-overlappingarea between the first outline and the second outline when the firstimaging data and the second imaging data are overlaid. In otherexamples, the area determined may be the overlapping area between thefirst outline and the second outline when the first imaging data and thesecond imaging data are overlaid. At operation 562, a second area isdetermined between the first outline and the third outline. At operation564, a third area is determined between the second outline and the thirdoutline. At operation 566, the first area and the second area areaggregated to generate a first aggregate motion score for the firstimaging data. At operation 568, the first area and the third area areaggregated to generate a second aggregate motion score for the secondimaging data. At operation 570, the second area and the third area areaggregated to generate a third aggregate motion score for the thirdimaging data.

At operation 572, reference imaging data is selected based on the firstcomposite motion score, the second composite motion score, and the thirdcomposite motion score. For instance, in examples where the areasdetermined in operations 560-564 are the non-overlapping areas of therespective outlines, the imaging data that has the lowest aggregatemotion score may be selected as the reference imaging data for motioncorrection. In examples where the areas determined in operations 560-564are the overlapping areas of the respective outlines, the imaging datathat has the highest aggregate motion score may be selected as thereference imaging data for motion correction. Thus, one of the firstimaging data, the second imaging data, or the third imaging data asreference imaging data for motion correction in operation 572. Atoperation 574, motion correction is performed on the set of imaging databased on the selected reference imaging data. The motion correctedimaging data may also then be displayed. The motion-corrected set ofimaging data may also be used in completing or performing the clinicaltask or review for which the imaging procedure was performed. Themotion-corrected set of imaging data may also be used for computing orgenerating final images, such as in MRI or tomosynthesis procedures. Forinstance, a reconstruction of a tomosynthesis volume may be generatedfrom the motion-corrected set of imaging data and/or tomosynthesisslices may be generated from the motion-corrected set of imaging data.

FIG. 6 depicts an example method for performing colorization of MRIimages. At operation 602, a set of MRI imaging data is accessed. The setof MRI imaging data may include at least a first volume acquired at afirst time and a second volume acquired at a second time. Additionalvolumes may also be included in the set of MRI imaging data. Atoperation 604, at least one local contrast enhancement region isidentified in at least two of the MRI images in the set of MRI imagingdata. For instance, a local contrast enhancement region may beidentified in the first volume and the second volume. At operation 606,contrast dynamics for the identified local contrast enhancement regionis evaluated for the MRI volumes for which the local contrastenhancement region was identified. Evaluating the identified localcontrast enhancement region for each MRI volume may include performing acolorization of each MRI volume by treating each MRI volume as if itwere the peak enhancement volume. For example, colorization of the firstMRI volume may be performed treating the first MRI volume as the peakenhancement volume, and the contrast dynamics for the identified localcontrast enhancement region may be evaluated. The same can be done forthe second MRI volume.

At operation 608, a peak enhancement volume is selected based on theevaluated contrast dynamics for the local contrast enhancement region.For example, selecting the peak enhancement volume may include acomparison of the evaluated contrast dynamics for the local contrastenhancement regions for each MRI volume in the set of MRI imaging data.At operation 610, colorization of the set of MRI imaging data isperformed based on the peak enhancement volume selected in operation608. The colorized set of MRI imaging data may then be displayed and/orstored locally or remotely.

FIG. 7 illustrates one example of a system 700 having a medical imagingapparatus 701 and a suitable operating environment 703 in which one ormore of the present examples of medical imaging may be implemented. Themedical imaging apparatus 701 may be any medical imaging apparatuscapable of dynamic imaging, such as an MRI apparatus. The medicalimaging apparatus 701 may be in communication with the operatingenvironment 703 and be configured to communicate medical images to theoperating environment 703. The medical imaging apparatus 701 may also bein communication with remote storage 705 and be configured tocommunicate medical images to the remote storage 705. The remote storage705 may also be in communication with the operating environment 703 andbe configured to send stored medical images to the environment 703.

The operating environment 703 may be incorporated directly into themedical imaging apparatus 701, or may be incorporated into a computersystem discrete from, but used to control, the imaging systems describedherein. This is only one example of a suitable operating environment andis not intended to suggest any limitation as to the scope of use orfunctionality. Other computing systems, environments, and/orconfigurations that can be suitable for use include, but are not limitedto, imaging systems, personal computers, server computers, hand-held orlaptop devices, multiprocessor systems, microprocessor-based systems,programmable consumer electronics such as smart phones, network PCs,minicomputers, mainframe computers, tablets, distributed computingenvironments that include any of the above systems or devices, and thelike.

In its most basic configuration, operating environment 703 typicallyincludes at least one processor 702 and memory 704. Depending on theexact configuration and type of computing device, memory 704 (storing,among other things, instructions to perform the image acquisition andprocessing methods disclosed herein) can be volatile (such as RAM),non-volatile (such as ROM, flash memory, etc.), or some combination ofthe two. This most basic configuration is illustrated in FIG. 7 bydashed line 706. Further, environment 703 can also include storagedevices (removable, 708, and/or non-removable, 710) including, but notlimited to, magnetic or optical disks, solid state devices, or tape.Similarly, environment 703 can also have input device(s) 714 such astouch screens, keyboard, mouse, pen, voice input, etc., and/or outputdevice(s) 716 such as a display, speakers, printer, etc. Also includedin the environment can be one or more communication connections 712,such as LAN, WAN, point to point, Bluetooth, RF, etc.

Operating environment 703 typically includes at least some form ofcomputer readable media. Computer readable media can be any availablemedia that can be accessed by processing unit 702 or other devicescomprising the operating environment. As an example, the operatingenvironment may include at least one processor 702 and memory 704operatively connected to the at least one processor 702. The memorystores instructions, that when executed by the at least one processorcause the system to perform a set of operations, such as the operationsdescribed herein including the method operations discussed above.

By way of example, and not limitation, computer readable media cancomprise computer storage media and communication media. Computerstorage media includes volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer readable instructions, data structures,program modules or other data. Computer storage media includes, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,solid state storage, or any other tangible medium which can be used tostore the desired information. Communication media embodies computerreadable instructions, data structures, program modules, or other datain a modulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media. Combinations of the any of the above should also beincluded within the scope of computer readable media. Acomputer-readable device is a hardware device incorporating computerstorage media.

The operating environment 703 can be a single computer operating in anetworked environment using logical connections to one or more remotecomputers. The remote computer can be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above as wellas others not so mentioned. The logical connections can include anymethod supported by available communications media. Such networkingenvironments are available in offices, enterprise-wide computernetworks, intranets and the Internet.

In some embodiments, the components described herein comprise suchmodules or instructions executable by computer system 703 that can bestored on computer storage medium and other tangible mediums andtransmitted in communication media. Computer storage media includesvolatile and non-volatile, removable and non-removable media implementedin any method or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Combinations of any of the above should also be included within thescope of readable media. In some embodiments, computer system 703 ispart of a network that stores data in remote storage media for use bythe computer system 703.

The embodiments described herein may be employed using software,hardware, or a combination of software and hardware to implement andperform the systems and methods disclosed herein. Although specificdevices have been recited throughout the disclosure as performingspecific functions, one of skill in the art will appreciate that thesedevices are provided for illustrative purposes, and other devices may beemployed to perform the functionality disclosed herein without departingfrom the scope of the disclosure. In addition, some aspects of thepresent disclosure are described above with reference to block diagramsand/or operational illustrations of systems and methods according toaspects of this disclosure. The functions, operations, and/or acts notedin the blocks may occur out of the order that is shown in any respectiveflowchart. For example, two blocks shown in succession may in fact beexecutrix or performed substantially concurrently or in reverse order,depending on the functionality and implementation involved.

This disclosure describes some embodiments of the present technologywith reference to the accompanying drawings, in which only some of thepossible embodiments were shown. Other aspects may, however, be embodiedin many different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments were provided sothat this disclosure was thorough and complete and fully conveyed thescope of the possible embodiments to those skilled in the art. Further,as used herein and in the claims, the phrase “at least one of element A,element B, or element C” is intended to convey any of: element A,element B, element C, elements A and B, elements A and C, elements B andC, and elements A, B, and C. Further, one having skill in the art willunderstand the degree to which terms such as “about” or “substantially”convey in light of the measurements techniques utilized herein. To theextent such terms may not be clearly defined or understood by one havingskill in the art, the term “about” shall mean plus or minus ten percent.

Although specific embodiments are described herein, the scope of thetechnology is not limited to those specific embodiments. Moreover, whiledifferent examples and embodiments may be described separately, suchembodiments and examples may be combined with one another inimplementing the technology described herein. One skilled in the artwill recognize other embodiments or improvements that are within thescope and spirit of the present technology. Therefore, the specificstructure, acts, or media are disclosed only as illustrativeembodiments. The scope of the technology is defined by the followingclaims and any equivalents therein.

1. A method comprising: accessing a set of imaging data including firstimaging data for a first time point, second imaging data for a secondtime point, and third imaging data for a third time point; identifying afirst location of a region of interest (ROI) in the first imaging data;identifying a second location of the ROI in the second imaging data;identifying a third location of the ROI in the third imaging data;determining a plurality of differences between the identified locations,wherein the plurality of differences include a difference between: thefirst location and the second location; the first location and the thirdlocation; and the second location and the third location; based on thedetermined plurality of differences, selecting one of the first imagingdata, the second imaging data, or the third imaging data as referenceimaging data for motion correction; and performing motion correction forthe set of imaging data with the selected reference image.
 2. The methodof claim 1, wherein the set of imaging data is magnetic resonanceimaging (MM) data.
 3. The method of claim 1, wherein the set of imagingdata includes two-dimensional medical images and the locations of theROI include a first coordinate corresponding to a first dimension and asecond coordinate corresponding to a second dimension.
 4. The method ofclaim 3, wherein determining the difference between the first locationand the second location comprises: determining the difference betweenthe first coordinate of the first location and the first coordinate ofthe second location; and determining the difference between the secondcoordinate of the first location and the second coordinate of the secondlocation.
 5. The method of claim of claim 1, wherein the set of imagingdata includes three-dimensional medical images and the locations of theROI include a first coordinate corresponding to a first dimension, and asecond coordinate corresponding to a second dimension, and a secondcoordinate corresponding to a third dimension.
 6. The method of claim 5,wherein determining the difference between the first location and thesecond location comprises: determining the difference between the firstcoordinate of the first location and the first coordinate of the secondlocation; determining the difference between the second coordinate ofthe first location and the second coordinate of the second location; anddetermining the difference between the third coordinate of the firstlocation and the third coordinate of the second location.
 7. The methodof claim 1, wherein determining the difference between the firstlocation and the second location comprises determining a distancebetween the first location and the second location.
 8. (canceled) 9.(canceled)
 10. (canceled)
 11. (canceled)
 12. (canceled)
 13. (canceled)14. A method comprising: accessing a set of medical images acquired at aplurality of time points; identifying a region of interest (ROI) in atleast half of the medical images in the set of medical images;identifying a location of the ROI in the at least half of the medicalimages in the set of medical images; comparing the identified locationsof the ROI in at least one pair of the medical images of the set ofmedical images; based on the comparisons of the identified locations,selecting one of the medical images of the set of medical images to be areference image for motion correction; and performing motion correctionof the set of the medical images with the selected reference image. 15.The method of claim 14, wherein the comparing operation comprisescomparing the identified locations of the ROI in a plurality of pairs ofmedical images in the set of medical images.
 16. The method of claim 15,wherein the plurality of pairs of medical images include all possiblepairs of medical images in the set of medical images.
 17. The method ofclaim 14, wherein the medical images are two-dimensional medical imagesand the locations of the ROI include a first coordinate corresponding toa first dimension and a second coordinate corresponding to a seconddimension.
 18. The method of claim 14, wherein the medical images arethree-dimensional medical images and the locations of the ROI include afirst coordinate corresponding to a first dimension, and a secondcoordinate corresponding to a second dimension, and a second coordinatecorresponding to a third dimension.
 19. (canceled)
 20. (canceled) 21.(canceled)
 22. A method comprising: accessing a set of imaging dataincluding first imaging data for a first time point, second imaging datafor a second time point, and third imaging data for a third time point;identifying an outline of a region of interest (ROI) in the firstimaging data; identifying an outline of the ROI in the second imagingdata; identifying an outline of the ROI in the third imaging data;determining a first area between the first outline and the secondoutline; determining a second area between the first outline and thethird outline; determining a third area between the second outline andthe third outline; aggregating the first area and the second area togenerate a first aggregate motion score for the first imaging data;aggregating the first area and the third area to generate a secondaggregate motion score for the second imaging data; aggregating thesecond area and the third area to generate a third aggregate motionscore for the third imaging data; based on the first aggregate motionscore, the second aggregate motion score, and the third aggregate motionscore, selecting one of the first imaging data, the second imaging data,or the third imaging data as reference imaging data for motioncorrection; performing motion correction of the set of imaging databased on the selected reference imaging data; and displaying at least aportion of motion corrected set of imaging data.
 23. The method of claim22, wherein: the first area is a non-overlapping area between the firstoutline and the second outline; the second area is a non-overlappingarea between the first outline and the third outline; and the third areais a non-overlapping area between the second outline and the thirdoutline.
 24. The method of claim 23, wherein selecting one of the firstimaging data, the second imaging data, or the third imaging data as thereference imaging data for motion correction comprises selecting theimaging data with the lowest aggregate motion score.
 25. The method ofclaim 22, wherein: the first area is an overlapping area between thefirst outline and the second outline; the second area is an overlappingarea between the first outline and the third outline; and the third areais an overlapping area between the second outline and the third outline.26. The method of claim 25, wherein selecting one of the first imagingdata, the second imaging data, or the third imaging data as thereference imaging data for motion correction comprises selecting theimaging data with the highest aggregate motion score.
 27. The method ofclaim 22, wherein the ROI is a skin line of a breast.
 28. A methodcomprising: accessing a set of MRI imaging data, the set of MRI imagingdata including at least a first volume acquired at a first time and asecond volume acquired at a second time; identifying at least one localcontrast enhancement region in the first volume; identifying the atleast one local contrast enhancement region in the second volume;evaluating contrast dynamics for the local contrast enhancement regionin the first volume; evaluating contrast dynamics for the local contrastenhancement region in the second volume; based on the evaluated contrastdynamics for the local contrast enhancement region in the first volumeand the second volume, selecting either the first volume or the secondvolume as the peak enhancement volume; and performing the colorizationfor the set of MRI imaging data based on the peak enhancement volume.